Şekil 1. Doğrusal olmayan sınıflandırma sorunu. Doğrusal fonksiyonlar,
tüm mavi noktaları turuncu noktalardan net bir şekilde ayırın.
"Doğrusal olmayan" etiketi içeren bir etiketi doğru tahmin edemeyeceğiniz
\(b + w_1x_1 + w_2x_2\)formunun modelidir. Başka bir deyişle,
"karar yüzeyi" bir çizgi değil.
Ancak, $x_1$ ve $x_2$ özellikleri üzerinde çapraz özellik yaparsak
sonra, iki özellik arasındaki doğrusal olmayan ilişkiyi bir
doğrusal model:
$b + w_1x_1 + w_2x_2 + w_3x_3$ burada $x_3$, iki nokta arasındaki
$x_1$ ve $x_2$:
Şekil 2. Ayrıca,
x1x2 ise doğrusal model
mavi noktaları turuncu noktalardan ayıran hiperbolik bir şekildir.
Şimdi aşağıdaki veri kümesini göz önünde bulundurun:
Şekil 3. Daha zor bir doğrusal olmayan sınıflandırma sorunu.
Özellik çaprazlama alıştırmalarında, bu verilere doğrusal bir model sığdırmak için doğru özellik çaprazlamalarının belirlenmesinin biraz daha fazla çaba ve deneme gerektirdiğini de hatırlayabilirsiniz.
Peki, tüm bu denemeleri kendiniz yapmak zorunda olmasanız ne olur?
Nöral ağlar, verilerdeki doğrusal olmayan kalıpları bulmak için tasarlanmış bir model mimarileri ailesidir. Bir sinir ağının eğitimi sırasında model, kaybı en aza indirmek için giriş verilerinde gerçekleştirilecek optimum özellik kesişimlerini otomatik olarak öğrenir.
Aşağıdaki bölümlerde, sinir ağlarının işleyiş şeklini daha ayrıntılı olarak inceleyeceğiz.
[null,null,["Son güncelleme tarihi: 2025-07-27 UTC."],[[["\u003cp\u003eThis module explores neural networks, a model architecture designed to automatically identify nonlinear patterns in data, eliminating the need for manual feature cross experimentation.\u003c/p\u003e\n"],["\u003cp\u003eYou will learn the fundamental components of a deep neural network, including nodes, hidden layers, and activation functions, and how they contribute to prediction.\u003c/p\u003e\n"],["\u003cp\u003eThe module covers the training process of neural networks, using the backpropagation algorithm to optimize predictions and minimize loss.\u003c/p\u003e\n"],["\u003cp\u003eAdditionally, you will gain insights into how neural networks handle multi-class classification problems using one-vs.-all and one-vs.-one approaches.\u003c/p\u003e\n"],["\u003cp\u003eThis module builds on prior knowledge of machine learning concepts such as linear and logistic regression, classification, and working with numerical and categorical data.\u003c/p\u003e\n"]]],[],null,["# Neural networks\n\n| **Estimated module length:** 75 minutes\n| **Learning objectives**\n|\n| - Explain the motivation for building neural networks, and the use cases they address.\n| - Define and explain the function of the key components of a deep neural network architecture:\n| - **[Nodes](/machine-learning/glossary#node-neural-network)**\n| - **[Hidden layers](/machine-learning/glossary#hidden_layer)**\n| - **[Activation functions](/machine-learning/glossary#activation_function)**\n| - Develop intuition around how neural network predictions are made, by stepping through the inference process.\n| - Build a high-level intuition of how neural networks are trained, using the backpropagation algorithm.\n| - Explain how neural networks can be used to perform two types of multi-class classification: one-vs.-all and one-vs.-one.\n| **Prerequisites:**\n|\n| This module assumes you are familiar with the concepts covered in the\n| following modules:\n|\n| - [Introduction to Machine Learning](/machine-learning/intro-to-ml)\n| - [Linear regression](/machine-learning/crash-course/linear-regression)\n| - [Logistic regression](/machine-learning/crash-course/logistic-regression)\n| - [Classification](/machine-learning/crash-course/classification)\n| - [Working with numerical data](/machine-learning/crash-course/numerical-data)\n| - [Working with categorical data](/machine-learning/crash-course/categorical-data)\n| - [Datasets, generalization, and overfitting](/machine-learning/crash-course/overfitting)\n\nYou may recall from the\n[Feature cross exercises](/machine-learning/crash-course/categorical-data/feature-cross-exercises)\nin the [Categorical data module](/machine-learning/crash-course/categorical-data),\nthat the following classification problem is nonlinear:\n**Figure 1.** Nonlinear classification problem. A linear function cannot cleanly separate all the blue dots from the orange dots.\n\n\"Nonlinear\" means that you can't accurately predict a label with a\nmodel of the form \\\\(b + w_1x_1 + w_2x_2\\\\). In other words, the\n\"decision surface\" is not a line.\n\nHowever, if we perform a feature cross on our features $x_1$ and $x_2$, we can\nthen represent the nonlinear relationship between the two features using a\n[**linear model**](/machine-learning/glossary#linear-model):\n$b + w_1x_1 + w_2x_2 + w_3x_3$ where $x_3$ is the feature cross between\n$x_1$ and $x_2$:\n**Figure 2.** By adding the feature cross *x* ~1~*x* ~2~, the linear model can learn a hyperbolic shape that separates the blue dots from the orange dots.\n\nNow consider the following dataset:\n**Figure 3.** A more difficult nonlinear classification problem.\n\nYou may also recall from the [Feature cross exercises](/machine-learning/crash-course/categorical-data/feature-cross-exercises)\nthat determining the correct feature crosses to fit a linear model to this data\ntook a bit more effort and experimentation.\n\nBut what if you didn't have to do all that experimentation yourself?\n[**Neural networks**](/machine-learning/glossary#neural_network) are a family\nof model architectures designed to find\n[**nonlinear**](/machine-learning/glossary#nonlinear)\npatterns in data. During training of a neural network, the\n[**model**](/machine-learning/glossary#model) automatically\nlearns the optimal feature crosses to perform on the input data to minimize\nloss.\n\nIn the following sections, we'll take a closer look at how neural networks work.\n| **Key terms:**\n|\n| - [Activation function](/machine-learning/glossary#activation_function)\n| - [Hidden layer](/machine-learning/glossary#hidden_layer)\n| - [Linear model](/machine-learning/glossary#linear-model)\n| - [Model](/machine-learning/glossary#model)\n| - [Neural network](/machine-learning/glossary#neural_network)\n| - [Nodes](/machine-learning/glossary#node-neural-network)\n- [Nonlinear](/machine-learning/glossary#nonlinear) \n[Help Center](https://support.google.com/machinelearningeducation)"]]
